
"""simple docstring"""




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class       __magic_name__							(							snake_case_			):
       UpperCamelCase_ =		(DPMSolverSDEScheduler,)
       UpperCamelCase_ =		10
       def 				lowercase_				(  self			,					**A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Dict   			=  {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**lowerCAmelCase__							)
         return config
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,					[0.00_02, 0.0_02, 0.02]							):
           self.check_over_configs(beta_start=lowerCAmelCase__			,					beta_end=lowerCAmelCase__							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=lowerCAmelCase__							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=lowerCAmelCase__							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Dict   			=  self.scheduler_classes[0]
         _lowercase:      Union[str, Any]   			=  self.get_scheduler_config()
         _lowercase:      List[Any]   			=  scheduler_class(**lowerCAmelCase__							)

         scheduler.set_timesteps(self.num_inference_steps							)

         _lowercase:      Any   			=  self.dummy_model()
         _lowercase:      Optional[Any]   			=  self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:      List[str]   			=  sample.to(lowerCAmelCase__							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Dict   			=  scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      str   			=  model(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      List[Any]   			=  scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__							)
           _lowercase:      Optional[Any]   			=  output.prev_sample

         _lowercase:      Optional[int]   			=  torch.sum(torch.abs(lowerCAmelCase__							)							)
         _lowercase:      List[Any]   			=  torch.mean(torch.abs(lowerCAmelCase__							)							)

         if torch_device in ["mps"]:
           assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75							) < 1E-2
           assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77							) < 1E-3
         elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06							) < 1E-2
           assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52							) < 1E-3
         else:
           assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62							) < 1E-2
           assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26							) < 1E-3
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Any   			=  self.scheduler_classes[0]
         _lowercase:      Any   			=  self.get_scheduler_config(prediction_type='''v_prediction'''							)
         _lowercase:      Tuple   			=  scheduler_class(**lowerCAmelCase__							)

         scheduler.set_timesteps(self.num_inference_steps							)

         _lowercase:      int   			=  self.dummy_model()
         _lowercase:      List[str]   			=  self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:      List[str]   			=  sample.to(lowerCAmelCase__							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Optional[Any]   			=  scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      Union[str, Any]   			=  model(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      Tuple   			=  scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__							)
           _lowercase:      Dict   			=  output.prev_sample

         _lowercase:      str   			=  torch.sum(torch.abs(lowerCAmelCase__							)							)
         _lowercase:      str   			=  torch.mean(torch.abs(lowerCAmelCase__							)							)

         if torch_device in ["mps"]:
           assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53							) < 1E-2
           assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84							) < 1E-3
         elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03							) < 1E-2
           assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97							) < 1E-3
         else:
           assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25							) < 1E-2
           assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21							) < 1E-3
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Any   			=  self.scheduler_classes[0]
         _lowercase:      Any   			=  self.get_scheduler_config()
         _lowercase:      List[str]   			=  scheduler_class(**lowerCAmelCase__							)

         scheduler.set_timesteps(self.num_inference_steps			,					device=lowerCAmelCase__							)

         _lowercase:      Union[str, Any]   			=  self.dummy_model()
         _lowercase:      Optional[int]   			=  self.dummy_sample_deter.to(lowerCAmelCase__							) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:      Union[str, Any]   			=  scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      str   			=  model(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      List[Any]   			=  scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__							)
           _lowercase:      Any   			=  output.prev_sample

         _lowercase:      str   			=  torch.sum(torch.abs(lowerCAmelCase__							)							)
         _lowercase:      Optional[int]   			=  torch.mean(torch.abs(lowerCAmelCase__							)							)

         if torch_device in ["mps"]:
           assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38							) < 1E-2
           assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35							) < 1E-3
         elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12							) < 1E-2
           assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71							) < 1E-3
         else:
           assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62							) < 1E-2
           assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26							) < 1E-3



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.scheduler_classes[0]
         _lowercase:      Any   			=  self.get_scheduler_config()
         _lowercase:      Any   			=  scheduler_class(**lowerCAmelCase__			,					use_karras_sigmas=lowerCAmelCase__							)

         scheduler.set_timesteps(self.num_inference_steps			,					device=lowerCAmelCase__							)

         _lowercase:      str   			=  self.dummy_model()
         _lowercase:      Tuple   			=  self.dummy_sample_deter.to(lowerCAmelCase__							) * scheduler.init_noise_sigma
         _lowercase:      Optional[Any]   			=  sample.to(lowerCAmelCase__							)

         for t in scheduler.timesteps:
           _lowercase:      Optional[Any]   			=  scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      Optional[int]   			=  model(lowerCAmelCase__			,					lowerCAmelCase__							)

           _lowercase:      List[Any]   			=  scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__							)
           _lowercase:      List[Any]   			=  output.prev_sample

         _lowercase:      Optional[int]   			=  torch.sum(torch.abs(lowerCAmelCase__							)							)
         _lowercase:      List[Any]   			=  torch.mean(torch.abs(lowerCAmelCase__							)							)

         if torch_device in ["mps"]:
           assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88							) < 1E-2
           assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11							) < 1E-2
         elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25							) < 1E-2
           assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11							) < 1E-2
         else:
           assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72							) < 1E-2
           assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11							) < 1E-2





'''simple docstring'''



import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCAmelCase_  :							Any             =    'http://www.mocksite.com/file1.txt'
lowerCAmelCase_  :							List[str]             =    '"text": ["foo", "foo"]'
lowerCAmelCase_  :							Any             =    '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Dict						  =					2_0_0
						_lowerCAmelCase  : int						  =					{'Content-Length': '100'}
						_lowerCAmelCase  : Optional[Any]						  =					{}







						def        __lowercase     (	self :   str					,     **lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													return [bytes(lowerCAmelCase__					,     '''utf-8'''					)]


def        UpperCAmelCase (    *A	:					int       ,		**A	:					Optional[Any]					):
							return MockResponse()
@pytest.mark.parametrize('''urls_type'''       ,		[str, list, dict]					)
def        UpperCAmelCase (    A	:					int       ,		A	:					Union[str, Any]       ,		A	:					Union[str, Any]					):
							import requests

							monkeypatch.setattr(A       ,		'''request'''       ,		A					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      URL
							if issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      url
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      [url]
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   str         =      {'''train''': url}
							SCREAMING_SNAKE_CASE       :   Any         =      '''dummy'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''downloads'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tmp_path
							SCREAMING_SNAKE_CASE       :   List[str]         =      DownloadConfig(
							    cache_dir=os.path.join(A       ,		A					)       ,		use_etag=A       ,		)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      DownloadManager(dataset_name=A       ,		download_config=A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      dl_manager.download(A					)
							SCREAMING_SNAKE_CASE       :   Dict         =      urls
							for downloaded_paths in [downloaded_paths]:
														if isinstance(A       ,		A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      [downloaded_paths]
																					SCREAMING_SNAKE_CASE       :   int         =      [urls]
														elif isinstance(A       ,		A					):
																					assert "train" in downloaded_paths.keys()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      downloaded_paths.values()
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      urls.values()
														assert downloaded_paths
														for downloaded_path, input_url in zip(A       ,		A					):
																					assert downloaded_path == dl_manager.downloaded_paths[input_url]
																					SCREAMING_SNAKE_CASE       :   Dict         =      Path(A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      downloaded_path.parts
																					assert parts[-1] == HASH
																					assert parts[-2] == cache_subdir
																					assert downloaded_path.exists()
																					SCREAMING_SNAKE_CASE       :   str         =      downloaded_path.read_text()
																					assert content == CONTENT
																					SCREAMING_SNAKE_CASE       :   str         =      downloaded_path.with_suffix('''.json'''					)
																					assert metadata_downloaded_path.exists()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      json.loads(metadata_downloaded_path.read_text()					)
																					assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type'''       ,		[str, list, dict]					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					Union[str, Any]       ,		A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      str(A					)
							if issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      filename
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      [filename]
							elif issubclass(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      {'''train''': filename}
							SCREAMING_SNAKE_CASE       :   Dict         =      '''dummy'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      xz_file.parent
							SCREAMING_SNAKE_CASE       :   List[Any]         =      '''extracted'''
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      DownloadConfig(
							    cache_dir=A       ,		use_etag=A       ,		)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      DownloadManager(dataset_name=A       ,		download_config=A					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      dl_manager.extract(A					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      paths
							for extracted_paths in [extracted_paths]:
														if isinstance(A       ,		A					):
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      [extracted_paths]
																					SCREAMING_SNAKE_CASE       :   str         =      [paths]
														elif isinstance(A       ,		A					):
																					assert "train" in extracted_paths.keys()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      extracted_paths.values()
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      paths.values()
														assert extracted_paths
														for extracted_path, input_path in zip(A       ,		A					):
																					assert extracted_path == dl_manager.extracted_paths[input_path]
																					SCREAMING_SNAKE_CASE       :   Any         =      Path(A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      extracted_path.parts
																					assert parts[-1] == hash_url_to_filename(A       ,		etag=A					)
																					assert parts[-2] == extracted_subdir
																					assert extracted_path.exists()
																					SCREAMING_SNAKE_CASE       :   int         =      extracted_path.read_text()
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      text_file.read_text()
																					assert extracted_file_content == expected_file_content
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					int					):
							assert path.endswith('''.jsonl'''					)
							for num_items, line in enumerate(A       ,		start=1					):
														SCREAMING_SNAKE_CASE       :   List[Any]         =      json.loads(line.decode('''utf-8'''					)					)
														assert item.keys() == {"col_1", "col_2", "col_3"}
							assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl'''       ,		['''tar_jsonl_path''', '''zip_jsonl_path''']					)
def        UpperCAmelCase (    A	:					Any       ,		A	:					str					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      request.getfixturevalue(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      DownloadManager()
							for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
														_test_jsonl(A       ,		A					)
							assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl'''       ,		['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']					)
def        UpperCAmelCase (    A	:					Dict       ,		A	:					Optional[Any]					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      request.getfixturevalue(A					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      DownloadManager()
							for num_tar, (path, file) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
														for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(A					)       ,		start=1					):
																					_test_jsonl(A       ,		A					)
							assert num_tar == 1
							assert num_jsonl == 2






def        UpperCAmelCase (    A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      DownloadManager()
							for num_file, file in enumerate(dl_manager.iter_files(A					)       ,		start=1					):
														assert os.path.basename(A					) == ("test.txt" if num_file == 1 else "train.txt")
							assert num_file == 2




